{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples and Exercises from Think Stats, 2nd Edition\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import random\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival analysis\n",
    "\n",
    "If we have an unbiased sample of complete lifetimes, we can compute the survival function from the CDF and the hazard function from the survival function.\n",
    "\n",
    "Here's the distribution of pregnancy length in the NSFG dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import nsfg\n",
    "\n",
    "preg = nsfg.ReadFemPreg()\n",
    "complete = preg.query('outcome in [1, 3, 4]').prglngth\n",
    "cdf = thinkstats2.Cdf(complete, label='cdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The survival function is just the complementary CDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import survival\n",
    "\n",
    "def MakeSurvivalFromCdf(cdf, label=''):\n",
    "    \"\"\"Makes a survival function based on a CDF.\n",
    "\n",
    "    cdf: Cdf\n",
    "    \n",
    "    returns: SurvivalFunction\n",
    "    \"\"\"\n",
    "    ts = cdf.xs\n",
    "    ss = 1 - cdf.ps\n",
    "    return survival.SurvivalFunction(ts, ss, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sf = MakeSurvivalFromCdf(cdf, label='survival')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13978014121\n",
      "0.86021985879\n"
     ]
    }
   ],
   "source": [
    "print(cdf[13])\n",
    "print(sf[13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the CDF and SF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fr7JpZ0WUK5NwUbiLxKkJxfnc84PLycrwTYRqaW3j54+s4LOte3s5U4YChbtIHCsZk8s9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21047ba890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Plot(sf)\n",
    "thinkplot.Cdf(cdf, alpha=0.2)\n",
    "thinkplot.Config(loc='center left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the hazard function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.676706827309\n"
     ]
    }
   ],
   "source": [
    "hf = sf.MakeHazardFunction(label='hazard')\n",
    "print(hf[39])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WuOm7iAnC6y+jM3elVFZIlf7msRBOZ8ieFFmhqsFdKRVXmZSWCW/mnhrtFjS4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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210449cdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Plot(hf)\n",
    "thinkplot.Config(ylim=[0, 0.75], loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age at first marriage\n",
    "\n",
    "We'll use the NSFG respondent file to estimate the hazard function and survival function for age at first marriage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "resp6 = nsfg.ReadFemResp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to clean up a few variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "resp6.cmmarrhx.replace([9997, 9998, 9999], np.nan, inplace=True)\n",
    "resp6['agemarry'] = (resp6.cmmarrhx - resp6.cmbirth) / 12.0\n",
    "resp6['age'] = (resp6.cmintvw - resp6.cmbirth) / 12.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the extract the age at first marriage for people who are married, and the age at time of interview for people who are not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "complete = resp6[resp6.evrmarry==1].agemarry.dropna()\n",
    "ongoing = resp6[resp6.evrmarry==0].age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function uses Kaplan-Meier to estimate the hazard function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def EstimateHazardFunction(complete, ongoing, label='', verbose=False):\n",
    "    \"\"\"Estimates the hazard function by Kaplan-Meier.\n",
    "\n",
    "    http://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator\n",
    "\n",
    "    complete: list of complete lifetimes\n",
    "    ongoing: list of ongoing lifetimes\n",
    "    label: string\n",
    "    verbose: whether to display intermediate results\n",
    "    \"\"\"\n",
    "    if np.sum(np.isnan(complete)):\n",
    "        raise ValueError(\"complete contains NaNs\")\n",
    "    if np.sum(np.isnan(ongoing)):\n",
    "        raise ValueError(\"ongoing contains NaNs\")\n",
    "\n",
    "    hist_complete = Counter(complete)\n",
    "    hist_ongoing = Counter(ongoing)\n",
    "\n",
    "    ts = list(hist_complete | hist_ongoing)\n",
    "    ts.sort()\n",
    "\n",
    "    at_risk = len(complete) + len(ongoing)\n",
    "\n",
    "    lams = pd.Series(index=ts)\n",
    "    for t in ts:\n",
    "        ended = hist_complete[t]\n",
    "        censored = hist_ongoing[t]\n",
    "\n",
    "        lams[t] = ended / at_risk\n",
    "        if verbose:\n",
    "            print(t, at_risk, ended, censored, lams[t])\n",
    "        at_risk -= ended + censored\n",
    "\n",
    "    return survival.HazardFunction(lams, label=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the hazard function and corresponding survival function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vPqqZS6uZGBVmdEKbVvlJuR99eiQKk2hosL3Fvn/vLjj7jCGG53Nzo+O0b5sY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\naGoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210354bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hf = EstimateHazardFunction(complete, ongoing)\n",
    "thinkplot.Plot(hf)\n",
    "thinkplot.Config(xlabel='Age (years)',\n",
    "                 ylabel='Hazard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dn5nZZ+6uW0/kCA3q1+Wai4dy3zMfAvBWzjyyh/TkuPYtAq5MpOYoMxjc/fpIH8Gv3f2T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20e7f51450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf = hf.MakeSurvival()\n",
    "thinkplot.Plot(sf)\n",
    "thinkplot.Config(xlabel='Age (years)',\n",
    "                 ylabel='Prob unmarried',\n",
    "                 ylim=[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantifying uncertainty\n",
    "\n",
    "To see how much the results depend on random sampling, we'll use a resampling process again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def EstimateMarriageSurvival(resp):\n",
    "    \"\"\"Estimates the survival curve.\n",
    "\n",
    "    resp: DataFrame of respondents\n",
    "\n",
    "    returns: pair of HazardFunction, SurvivalFunction\n",
    "    \"\"\"\n",
    "    # NOTE: Filling missing values would be better than dropping them.\n",
    "    complete = resp[resp.evrmarry == 1].agemarry.dropna()\n",
    "    ongoing = resp[resp.evrmarry == 0].age\n",
    "\n",
    "    hf = EstimateHazardFunction(complete, ongoing)\n",
    "    sf = hf.MakeSurvival()\n",
    "\n",
    "    return hf, sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ResampleSurvival(resp, iters=101):\n",
    "    \"\"\"Resamples respondents and estimates the survival function.\n",
    "\n",
    "    resp: DataFrame of respondents\n",
    "    iters: number of resamples\n",
    "    \"\"\" \n",
    "    _, sf = EstimateMarriageSurvival(resp)\n",
    "    thinkplot.Plot(sf)\n",
    "\n",
    "    low, high = resp.agemarry.min(), resp.agemarry.max()\n",
    "    ts = np.arange(low, high, 1/12.0)\n",
    "\n",
    "    ss_seq = []\n",
    "    for _ in range(iters):\n",
    "        sample = thinkstats2.ResampleRowsWeighted(resp)\n",
    "        _, sf = EstimateMarriageSurvival(sample)\n",
    "        ss_seq.append(sf.Probs(ts))\n",
    "\n",
    "    low, high = thinkstats2.PercentileRows(ss_seq, [5, 95])\n",
    "    thinkplot.FillBetween(ts, low, high, color='gray', label='90% CI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot shows the survival function based on the raw data and a 90% CI based on resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gU0Kqk/TcBRhj2L9/P5edu4CLVs/3bP/DK1tpaG7zY4RKKV/TBDCJZWVlYbVasdvtFBUV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\nfFcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20e744ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ResampleSurvival(resp6)\n",
    "thinkplot.Config(xlabel='Age (years)',\n",
    "                 ylabel='Prob unmarried',\n",
    "                 xlim=[12, 46],\n",
    "                 ylim=[0, 1],\n",
    "                 loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SF based on the raw data falls outside the 90% CI because the CI is based on weighted resampling, and the raw data is not.  You can confirm that by replacing `ResampleRowsWeighted` with `ResampleRows` in `ResampleSurvival`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More data\n",
    "\n",
    "To generate survivial curves for each birth cohort, we need more data, which we can get by combining data from several NSFG cycles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "resp5 = survival.ReadFemResp1995()\n",
    "resp6 = survival.ReadFemResp2002()\n",
    "resp7 = survival.ReadFemResp2010()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "resps = [resp5, resp6, resp7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is the code from `survival.py` that generates SFs broken down by decade of birth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def AddLabelsByDecade(groups, **options):\n",
    "    \"\"\"Draws fake points in order to add labels to the legend.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    thinkplot.PrePlot(len(groups))\n",
    "    for name, _ in groups:\n",
    "        label = '%d0s' % name\n",
    "        thinkplot.Plot([15], [1], label=label, **options)\n",
    "\n",
    "def EstimateMarriageSurvivalByDecade(groups, **options):\n",
    "    \"\"\"Groups respondents by decade and plots survival curves.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    thinkplot.PrePlot(len(groups))\n",
    "    for _, group in groups:\n",
    "        _, sf = EstimateMarriageSurvival(group)\n",
    "        thinkplot.Plot(sf, **options)\n",
    "\n",
    "def PlotResampledByDecade(resps, iters=11, predict_flag=False, omit=None):\n",
    "    \"\"\"Plots survival curves for resampled data.\n",
    "\n",
    "    resps: list of DataFrames\n",
    "    iters: number of resamples to plot\n",
    "    predict_flag: whether to also plot predictions\n",
    "    \"\"\"\n",
    "    for i in range(iters):\n",
    "        samples = [thinkstats2.ResampleRowsWeighted(resp) \n",
    "                   for resp in resps]\n",
    "        sample = pd.concat(samples, ignore_index=True)\n",
    "        groups = sample.groupby('decade')\n",
    "\n",
    "        if omit:\n",
    "            groups = [(name, group) for name, group in groups \n",
    "                      if name not in omit]\n",
    "\n",
    "        # TODO: refactor this to collect resampled estimates and\n",
    "        # plot shaded areas\n",
    "        if i == 0:\n",
    "            AddLabelsByDecade(groups, alpha=0.7)\n",
    "\n",
    "        if predict_flag:\n",
    "            PlotPredictionsByDecade(groups, alpha=0.1)\n",
    "            EstimateMarriageSurvivalByDecade(groups, alpha=0.1)\n",
    "        else:\n",
    "            EstimateMarriageSurvivalByDecade(groups, alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the results for the combined data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Zs8jt0vnU+jkU+00MTSNn2zyxp5cdrVEs+41zIj6TNXMiLK0K4jV0bAnRVI7N\nLUPs7hg5ZZ98ps6soImpaRiaIG/bRFMWg8ksgwl116AopxKPx4nFYsRiMXp7e/H5fNx5550AM7K8\npwoMM1TA5+bum9dS5Pfgcumks3mee+Uwbd1DnOg59ea02hI/V84rxW1oCFmYiH5idzf7Ot9IoKdr\ngppiL+9pKmNumRfDAFvCQCLHb/b1nLJ+g6ZplAa9FPtcuHQNKSBrWQyn83TH0ifNZyiKMrlf/epX\nlJeXs27dOoAZWd5zJmxwU05h1bIGrl41lydeOEQ+b9M/HOeV147SWFtKfVVk0hoJQgj+6Kp6mvsT\nNPfEyOYdDvUk+M2OTmrCXspCHqCQF6ku4uN9C8pIZm2OR9PkbGgbyvCzV7v42JoazEk2sJX7TaJJ\nk5wtiaUdco4kYzlE03kGElnqI+qvlDLz/PffHj5n1/q7m96a7u3s/PSnP+XjH//4+OOZWN5T3THM\nYOGQj7tvWUdTXTkul45lSfa39nL0xAD90VNXV6sr8fOZ9bMp8RfSZWTzNpuPDLBxb89JNRkiPpM5\nZUFWzw4T8hTmG+JZi9c6R3lkd/ekE92GrlEX9lERdOMzC6uULMchnrHojWdU3QZFOY329na2bNnC\n3XffPX5sJpb3VIFhhmuqr+CD112Kd2zSdySWYteBEzS39Z32vCuayrj1skqCHgMQjKYtNu7qYd9b\n5hBmhdysqCtmfrkfr0vDlpKhVI6tbSMc7p08+ATdBpVBD6V+F6YmQELeshlJ54mmVGBQlFN54IEH\nuPLKK6mvrx8/FggEZlx5T3XfP8O5XDrXX7mEXzyxnQPHeslkLA629rC/uYtlTTWEgpPnOhJC8Ifr\nGjjSk+DFlkGyOcnxgRS/3dnJnLIAIV9hB7QmBHURH+vmREjlHJoHEuQtSX88x8aD/TSWBTANbcK1\nS3wm8WyeeNYim8xhSUkqb9MTS1EaUHmUlJnl9x3+OVceeOABvvGNb5x0bPHixdx///3jj2dCeU91\nx3ARCBf5uWbVAvweE02DwZFEITgcPfVuaCjsPfjyDU3MCnrQNIHtOLzUMsQLR06+2zB1jRX1YdbM\nDlMRciMpzBs09yd4eHf3pEtRNU0Q8bkJe03cWmH5as6SDGcsemI5lS5DUd7i5Zdfpru7mw9+8IMn\nHb/ttttmXHlPFRguEu+/agnlkSCmyyCbszl4tJute1oZHD71XANAZbGPO1ZX4zMLN4fRZJ4n9vSd\nVNQHCpOnT1UgAAAgAElEQVTRqxvCXFIZwmcW5htimTxb26L8Zk8vziQf9EUeg1K/ic/tQtcEDpJk\n1qJjJMlIWi1fVZQ3++lPf8odd9yB3+8/6fhMLO857aU9z5V3Q3bV07Fth6/d9xDPvnyYaDyJ2+Xi\nqhXz+OgHVnHt2oWnPTeVyfMXP32NA11x8rZDwGNwx+oaPnlVA0HvG6VDHSnZ2zXCr3f10DyYIm9L\nTENQGXJz85JZbGgqm3DtrGWzu2uUrtE0WdvBEIKA20XEZ7CyuhjDUENKyvRT2VUvruyqyjmi6xp3\nXLeS0nAAj+kinclzqK2H7fvaGImlTnuuz+Pio1fUU+RzoWmCRNZiy8F+dh4/OVehJgQNJQEurS2i\nPuLFpUHekvTFcjxxsJ8d7dEJf/Hchk55wE3Q48KtCywpyeRtRtIWx4fTk95pKIoys6nAcBG5/JIG\n1iybQ8jvQdOhfzDGvqPdHGo9fSoLgKsXlrN6TgSPoYOEjmiGjbu7GUmdPOQT8hgsqy5mTX2Y6mIP\nQkDWcugdzfLwnh4OTbJSqSLgJuQ28I8tX81YNpm8zYmRFEf7E8Qz+VPu1lYUZeZRgeEiIoTgY7dc\nTlVFGNPlIpuzOdbRz9Y9x8mfYf+AS9f4oyvrqYl40TUNy3HY0TbMtqMn514SQlAd9rCoMsSKmiKq\nxjbEJfM2HcNZHtzZRTR5crptr6lTGXITcBv4XRqaBhlbks5ZdMbStEZTtA+nsVRwUJSLggoMF5mm\n2bNYt3wORYHCt/mhaILdhzvY33L6FUoA8ytDfPSKOnxm4Y89lrH5zc4uuofTJ7VzGzoNJT5W1YdZ\nXBWkImiOp9puj6Z5bH//Se2FEJQHPFQE3PhMA4+hI3DI5CWpnEVPLEvbcGrC3YmiKDOTCgwXoZuu\nWUplWRFul0EmZ9Pc1svmHc2k0tkznnvd0koWVYdwaRrSkezpGGXj7i7Sb8lzZBoas0v9rGuIsLym\niIjPhSMlyZzNq23DE4KJx6VTHnRT4ncTdBeCg4MknXfI5C0SOYuu2Jn7pyjKhacCw0VoYWMVa5fP\nIeB3owkYGIqz53AHe490nvFc09D41NUNhHwGAkE6X8jA+uTe3gnJ80xdY15FgKvnlrCsKoihCRwp\nGUzmeGTfxHmNoNugKuQh7DPxmgZ+t44EMpZDJmczkMiQydvn6m1QFGWaqMBwkbr9vZdSU1GM6TbI\n5h0OHuvh5V3HSE1h/8ClDRGuairFY2ogoSua4cm9vbx4ZJDsWz64TV1jXnmAq+aWUBF0A5C3YXdn\njB0nTi4CJIQg4jOpKfJS6jfxmwYBU0NKSd6RJLI2UTWcpCgzngoMF6mm2RWsW9FYWKGkwdBoit1H\nOtm278yZF3VN8In1DdSWeHEbGpZ0ONgZ4/HdPRzqik9YkqoJwYJZQVbWFeE2BBLJaCbPb/f1crD3\n5PaaJij1mzSV+pkVcBN0u9DH/pZlLYfuWJpYWpUDVZSZTAWGi5Smady4fim1s8K4TYNczqL5eB8v\n7mwhOjp5beg3q4n4uHN1LWVBN4amkcpb7D4xwmO7OhmMT5wLcBs6l88OUx/2IYTAsiXHB5M83zxA\n/1vaCyHwu13MKw1QW+zFNDQEYEtJXzxLx2iaoWTupEyviqLMHCowXMSa6itYvbSBUMCL0GA4nmJ/\nSzebX20+4y5QIQQ3LKvk/UtnEQkU9h8kMhYvNg/ycssgOWvi0tLG0gCX14eJ+AyEECTzDrs7Y2xr\nG5m0va5rVBd5KfW5McZqRadyDj2jGQaTWfoSWSwVHJR3gfb2dv7gD/6ASCRCVVUVf/EXfzG+t2em\nlfUEFRguah63i3Ur5lI7K4zf4yaTy9PWNciLu47R1XfqKm/j55sGd11ey/r5ZXhdOlIWcik9vL2T\ntv6Jdx2moXFFY4QVNcX4XYWNciNpixePDXKob/KcTYauUV3kwefS0QVYjsNwOkdfPEM8k2NUDSsp\n7wKf//znKS8vp6+vj927d7N582Z+8IMfzMiynqACw0VvxcI6ViyqoyjoRROCaCzN/pYufrft8JR2\nG5eGPHx4bR2La0IYmsB2JM19SX6xrYP4JB/aFSEP1y0qZ06ZH0Mv5FfqHMmwuXmAoeTEISghBKUB\nNzXFXkyXji4EtpT0J3J0jGToT2ZIZPPv+jw3yjtbW1sbd911Fy6Xi/Lycq6//noOHDgwI8t6gqrH\ncNEL+j285/IFtJ4YIJHMMJrI0NM/wiu7W1m3Yi6NteVnvEZ9WYA/vKKe7uEMXcNpcrbDS82DLK0r\n5rpLZuExT06EVx/xcd2CEvpHM/Qmc2RtyZ7uBI3Ho1y/aBa6dnK+roBp0FASIG85nBjNYNmQtx2G\n03k0AXnLoSrkJeIz0bSzyvWlKFPybHP/mRtN0Xubzvxv6q2+9KUv8eCDD7J+/Xqi0ShPPPEE9957\nL88///yMK+sJ6o7hHWHl4nouXzaHitIiDEMnlspypLWPTa8emXKOotVzS7hxeQU+9xtDSo/u7GJ3\n+zDOW+YBdE2wrCbM2sYwPkNHSkk8neelY8McG0xOuLYQgpDHRVNFkFlBN26XhhCF4BBN5emL52ge\nTNAykCCRVRXglHeeq666iv379xMKhairq2PVqlXccsstM7KsJ6jA8I7gcbt43xULWTy3kqDPjW1L\n+qKjbNvTxvGuoTNfgEIupRuXV7OwMoipCxzp0NwT5+EdHbRP8mHvNXWunBOhscxTGIICTgynebFl\n8JTLUUMek4UVQcr8Jm5dQxOQswtzDkOpLN2xNG3RpMrIqryjSCm5/vrr+eAHP0gqlWJwcJBoNMpX\nv/rVGVnWE05Tj0EI8VvglP9CpZQ3T1enJvNur8dwJlJKHnluN//yi800tw3g4FBfGeGzH7qaj35g\nNUKceYjGcSSbD/Xz3Seb6R3N4iAJuA1uXVnFZ65pxO8+eeQxnbPZ3j7ET7d3MZyyEEB50MWHllez\nfl4Jmjb5945UzuK1zmEGk3nyto3lgBDg1jWCbheXVoeI+N1T6rOiwMyuxzA0NER5eTkjIyPjH/iP\nPvoo3/rWt/jCF77AT37yE1588UWgUNazrKyM3bt309TUNH6N18t63nfffadctXS+6jF8B/gH4DiQ\nBv517CcBHDubJ1GmnxCCtcvnsKSpmoDPxLYkA8MJXtzVQv/Q6au8vU7TBKvnlnDzikrCPhdCQjJr\nsenQAC8dGZgwpOQ1deaVh7i0OohHL+xwjqYsXmqNcnRg4l3G63ymwdKqImqKPQS9Jh6XhpSF1Bmx\nbJ7BZI541pqx/9AV5WyUlJTQ0NDAD3/4Q2zbZmRkhPvvv59ly5Zx6623zriynnCawCCl3Cyl3Ays\nk1LeJaX87djPR4GrzkvvlLMyq7SIq1c0Mas0hGEIUpkcLcf72LqvdcrX8LsNblpRzRXzS/GaGo4j\n6R/N8ottHRzpjU1oXxowWdtQQmWRG10rbHxrGUiy+eggydPMF4Q8Jssqi6gdS5/h0gtfaDKWQ1cs\nw0gmRzSVJ523VYBQLnq//vWv2bhxI2VlZTQ1NWGaJvfdd9+MLOsJUyjtKYQ4BPyBlLJ17HEDsFFK\nefp6kueYGkqamt7BUf7uhxv53bYjxBIZSsJ+3rt2AX/1x9dRHglN+Tp7Toxw3xNHONIdJ+84FHkN\nVjeU8K3bFuH3uE5q2x/P8krrEI/u7WM0a6ELiPhMPryykivnlmKcYkgJIG/Z9CSyHOgdZTRt40iJ\nx9CYFXTTWBpA1zRMXRB0G2poSTmlmTyUdL6c79KeXwY2CSE2CSE2A88DXzqbJ1HOn/JIkCVNVVSU\nhtB0SCQzHGjuYffhTvJnkdl0SU0R71tUTmWxBw1BPGOzv3uUR3Z2T/jLVxowWVxVxLLqAB5dw3YK\nuZSebxlkf1fstP9gXYZOXbGPcr8H19juaEtCfyLHscEEmbxFzpbEs5aalFaU8+SMgUFK+SQwD/gi\n8AVgvpTyqenumPL2aJrG6iWzmVdfhsd0kbcdeqOjvLLrGJ190TNfYIyuCT6wopqVDRGKvC4cRxKN\n5Xhqbw9H+07eFa0JQV3Yy+qGEmrDHnQhyNmS1sEMzzf3c7gvccYP9VkhN2GvC10TOI7ElpKBZI7m\n/jg52yZnS0bT+QnzHIqinHtnDAxCCB/wl8CfSyn3AHVCiA9Me8+Ut23e7AqWzK2mpDiAJiCVzrFz\nfzvt3VGio6eeFH6rYr/JzSsqWVAVxKVrZG2HzmiKh7Z3YNkn748wDY0FFQEuqy+i2OdCANm8zb6e\nQqK9rpH05E8ypshjEgl4CLkNNA3ytiRnOYxkLI4PJcjbNraERE7tc1CU6TaVoaT/AHLA2rHHXcC9\n09Yj5fcW8HlYuaiOuXWluAydXN6iLxpjx752OnqHyeamnp/oktpiNiwsoyJUWD4ay9psbRlk69HB\nCW3DPpMVdWEuqy0i4NZxKKxq2tM1ypaWQdK5Uw9lBdwGlUE3s4JuSnwuXDrYEnK2ZDiVp2skTXps\nWOmtQUlRlHNrKoGhUUr5bSAPIKVMURgKVmawBY2zmFdfQaTIj0CQSOd4ZU8riWSGrr6RKV9HCMH1\nS2exsiGMzyyUA+2L5fjFKx0MxjMT2lcXeVhZV8zCigA+U8dyCrWlt7WPsr9ndNIsrK+rCHpoiPip\nCHiZFShkZAVIWw6DyRzdoylSOYt0XgUGRZlOUwkMOSGEl7HNbkKIRmDKxXuFENcLIQ4LIZqFEF89\nRZsNQohdQoj9Qojnp3pt5dTCIT/L5tfQUF2CrmnkchZd/cM89eJ+hmOpKVV6e13Aa3LHqmoaywNo\nQpCzHA50x/ivrZ0Taiq4DZ15FQFWzw7TVOrD0Me+9SdzPHlggPZo8rR1GIp8JgsqAjSU+Am4jbHE\nfpDI2YykLfriaVJ5S6XrVpRpNJXA8NfAk0CtEOJnwHPAX03l4kIIDfhn4DpgMfARIcSCt7QpAr4P\nfEBKuQT40NS7r5yKEIIVi+porCunJBzAtiXJVJYtO47y8q6jDAxPbdPb6xbXhnnfJbMo8ZswVrvh\nuQN9bD82NGFCOORxsbS6iGW1IaqKPQgK3/pboykeP9BP10jmtJPRhqZRGfJSU+TBb2oYeqHWdCxr\nM5zKM5LOkla1oxVl2kxlVdIzwO3AJ4D/BC6TUm6a4vVXAy1SynYpZR54ELjlLW0+Cjwkpewae76J\ng9fK21JdEWb1JQ0saJiFx6OTyeUZSaR56sWD9A/Fppxg73Xvv6SCSxvCuI1Ceu6+WJZ/3Xyc7a0T\n8zFFfC5W1IRZMitExFtIpZHKWuzvjrGjY4iBxOnvWIQQzI74qSzy4jE0hBDYjkMsazMYz5LMqbsG\nRZkupwwMr3+zF0KsAOqBHqCbwqqkFVO8fjXQ8abHnWPH3qwJiAghnhdCbBdCfGyqnVdOTwjBmmUN\nNNaXsXhuNSBIprP0DsXYsrOFvimmynhdadDDdUtnMacsgKZpZCyb1v4EP3q+la7oyaudhBBUh72s\nqi9mWU0Iv6ljS0jmbJ5vjtI+lDzjJLLPNKgMeoj4TNy6AASW4xBN24ymciTVCiVFmRanu2P4yth/\n/2GSn++cwz4YwArgBuB64FtCiLnn8PrvamWRIOsubWRuXTklxQFsyyGRyrJlewvtXQNnNdcAsKax\nhCubSqgq9qCNDSm1DiT4xydbSE+SAmNBRZAlVUUsqvBjaJCzJNFEno0H+xlMnv65NSGI+EwaIl7C\nXhNjrFZD2rLpGE0xms6TsdSQkjLzHT58mGuvvZbi4mKampp45JFHxn83E0t7nrJQj5Tys2NzBN+U\nUr70Nq/fBdS96XHN2LE36wQGpZQZICOE2AIsA46+9WL33HPP+P9v2LCBDRs2vM1uvXu8PtcwMJSg\no3uIweEE2Vye7oERXtrVSmk4xNz6qRce8Zg6111SyWja4uXmQbpGMiQyNns6RvnPre18an3jSe1N\nQ2NpdYhMzqI/mac9miZjOxwbSLG7Y5i1c0op8rpO8WyFdOAVAQ/JnE0il2c0I5ES4lmbtmgSXRNU\nhTwqXYYyY9m2zS233MLnP/95nn32WTZt2sRNN93E7t27CYfD3HHHHfz7v/87H/jAB/jmN7/JXXfd\nxSuvvPK2n2/Tpk1s2rTp9+rzVHIl7ZJSXvq2Li6EDhwBrqUwFPUq8BEp5aE3tVkAfI/C3YIb2Abc\nJaU8+JZrqVxJv4e2rkF+9tg2fvO7vfRH47hNgwUNs/hvn3wfS+fXEAp4z+p6e04M8/S+Pp7a28tw\nModL16gv9fG/P3IJdaXBCe3741k2twzw2P5+EjkLgaCm2MPdl9ewvKb4jB/sA4ksJ0aSnBhOk7Uc\ndCHwmQblQTeLywP43KcOLso730zOlXTgwAHWrl17Ut2F6667jjVr1lBTU8P9998/nnY7lUpRWlo6\nnnZ748aN/OVf/iUdHR0UFRXx5S9/ma985SuTPs+5zJU0ldKezwkh7gB+fbafzFJKWwjx58DTFIat\nfiylPCSE+Fzh1/JHUsrDQoingL2ADfzorUFB+f3VV5Vw2eLZHDzaTXQkST5v0d0/zBNb9lFSHCDo\nP7tv3cvqwghgMJ5j06F+8o5Dz0iG//vccb55yyL8npP/apUH3VxWH6F1IMXOzlEsR9IXz/Jc8yBz\nSgOnvWsACHkMgm6TsMdiKJ3HtiUZy2YokWUoYKrAoJzW4e7EmRtN0YKqwO99DSkl+/fvZ3R09KIt\n7fk54JdAVggRE0LEhRAT8y+fgpTySSnlfCnlPCnl/xo79i9Syh+9qc13pJSLpZRLpZTfO+tXoZyR\nEIJLF9ayYE4lpZFAYTgmkeXlPW3sPNjO0MjUU2W8bnFNMesXlFId9oAUpPM2BzpH+OWrHZO2nx3x\nckVjhMqgGyEK+xsO9iRo6YuTP8NEtNvQiXhdFPvNsUyrYDmSVN6hL5FTK5SUGWv+/PmUl5fzne98\nB8uyePrpp9m8eTOpVOriLO0pCl8hF0spNSmlKaUMSSmDUsqp529WZoyySJBLF9ayqLESn8ckZ9tE\nR+I8vnkfx070n1WqDCgk2ruyqYyVDWEi/kKivf5Yjs0H+znQMXF3tRCCRZUh1swpxm0UbnsTWYsX\njw/ReYa9DVBI5V3scVHsMTD1wvm2U0iZkVIrlJQZyjAMHnnkER577DEqKyv57ne/y1133UVNTQ3B\nYHBGlvY87VCSlFIKIR4HLjkvvVGmlRCCyxbPprm9n/6hOIdbe8jmbFo7Bnlx11GKgj7mN1Sc1ZBS\nyOfi5hVVdEWz7GqLknMc2qJJ/uvVTv77rCCm6+SKUxGfi9X1EXa0j9I+nMF2JAd6kqyoTlLkMYj4\nzVM+l6YJwj4XeVuSyNqkrSwgSeUtBpNZAm4DTU1CK5M4F8M/v48lS5acNCG8bt06PvGJTwBw//33\njx9PJpMcO3aMxYsXA7By5UoeeeSR8dKed95553lZtTSVoaTXhBCrpr0nynlRXhJkYUMlS+ZXUxz0\nkbdtUpkcv9t6hP5o7Kz3NgAsri5mw8JSKorcCAmJjM3e9mGeOdA3oa0QgrqIj9V1hbsGhCCWzvNs\nyyC9sQyZM+xoDpouDF0Q8rnQhcCSEsuRDKVyDCZzqmaDMiPt27ePbDZLKpXiO9/5Dr29vXziE5/g\ntttuu7hKe77J5cArQohjQoi9Qoh9Qoi9090xZXpomsbKxXXUVURYOK8STRPkcoXsq0+9cIDu/hEy\n2bMbUtI0wfuWVLB8djFBT2FIqS+W58k9PQzEJibac+kaV8wtoabYiybAcqBtMMWuzhG6RzOnXV2i\naYJij4nPpeE2NAQUakUkcvSMpokmszN2dYry7vXAAw9QWVnJrFmzeP7553nmmWdwuVwXdWnP+smO\nSynbp6VHp+6HWq56Dr128AS/eX4Pz7xykJ7+GD6Pi/JIkE/dfiXrVjTSUFN61tfc1Rbln55u4UBX\nHNtxKPGZ3H31bO5cU4dLP/k7iO1IHt3Xw+P7+xlNFwJRTbGH25ZVsqgySFnAfcrncaSkYzjNkYEY\nIykLhMSt6/g9BmU+k7qID7/LwDSm8r1HeSeYyctVz5fzWtpzLM9RO5CmkGH19R/lIrZkXhXz6itY\nNKcKj+kim7MYTaTZuGUf/dH4Wd81ACytLebKpjKKPTpCwmjG4rkD/bT0Thye0jXB2vowiyr8uHSB\nZKx29PEox4dSjKRP/fyaEIS8BkUeF6ahoSGwHEkmbxNN5zkRTdETy5DOqwlpRXk7plLB7WYhRAtw\nHNgMtAFPTHO/lGlmugw2rJpHQ00J82aXISUk0zmOdw1ysKWLgeGzX/et6xrvWVTOguoiXC4Ny3Y4\nPpDgqb29tPbFJ3ybmVXkYX1TKWV+E2Ns+eqRvjivnRimpf/05UADLp2g20WRx8Bj6iAgbzlk8w7R\nVJ6u0RQ9MTWspChvx1Tutf8WWAM0SykbKOxiPj9rppRpVV0RZtWS2TTWlhP0ucnlbGKJDM9uPUxn\nb/Sss68CzC7zc+3iCiI+E00IklmbF5sHeXp/P51DqZPaCiFYOCvI2jnhsRVFkMw67OyIsat9mONv\naf9mLkMn7HXhdRl4XQZurXAba0kbB0nakkRTOZVLSVHehqkEhryUcgjQhBCalPJ54LJp7pdynqxZ\nNoclc2cxuzqCrgtSmRxHO/rZvr+dwbex6U0IwYaF5SytK8Jn6kgJvaNZXjgywPOH+om+Jd223zS4\noqGERbOCeAwNKSCWzbO7J86+rhgnhk8dHEJeF+UBE7+pY+gGXkNDRyNn2TjSIZ611P4GRXkbphIY\nRoQQAWAL8DMhxD8CZ/+JocxIQb+Hq1ctYOmCWoIBD450iMWzvLzrKCe6h7DfRn3lIp+LG5ZV0lge\nwGvqZC2bE4Mpnj/Uz+ZD/RN2OVcVe1jbGGbBrCCmJrAsGErlefHYELs7RollJp9vcOsaIa9J2OvC\nbWggxor6OJDOOWQtZ3xiW1GUqZtKYLiFwsTzlylUcjsG3DSdnVLOr7l1Zay6ZDaNtWUYuk4qm6W9\nJ8qe5k7auycW4ZmKyxtLuGZRObUlXjy6Rjpv0T6Q5LmD/RzvO3n+wqVrLK8uZk1DhKpiD6YBliXp\nGc3ycmuUw33/P3tvGivXed9pPu/ZT+3b3TfycpNIkaJWS7Jky0ts2XHirJ3EnWkgPTNJD5BO+kMG\n3RlgkKR7gKSBQSMNBJ1exkkm6enYju3YcexYXhRt1kaJoiRK3MnLu++1V539nQ+ndEWKlH15KcoU\neR6ggHvurTr1kqg6v/P+t9/l8w2aqmBpCnlLJ21o6JogiOKKpyCM8MOIatdP8gwJCVfIZqqS2lLK\nEEgB3wD+O0lV0g3HBw/uYN+OIbIpC4GgVu/wzEtnWFxtUG92r/h8pq7y03eN8JFb+hku2uiqQssN\nObnQ4OuH53DeFuJJGSoPTpZ4YLLIQNZC12Lfhdm6w9On1zj5DuKQMTUqGYO+jEFKV0kZChESN4qF\noeaEzNa6eEmu4YZmYmICIcRN/ZiYuGxnwZb4kdNVe5NQ/wBwgAgQxMIw+a6tIuHHTjGf5oE7dvLC\n0fO0Oi6uH3BiaokjJ2Yo5FLkMlfueZBP6XzmzmGqHY+OF7JYc6h1fA6drfLVQ/P87D3D2MZbH0FD\nU/jY7n68QPLU6XUWmi4dL+TkSpvyVBUhYM/ApSO9NUVhJG/hBiE1JyClq3SDWBicIOB8rUskJROl\n9FX/PyVcn0xNTf24l3BDsZmx278D3JZ4Md/43HtgG/t2DjG/VKPR6rJeb/H4CyfZOzlEKZ9msHLl\nsxMHCzYfv22Q1ZZPw/FpOwGLdYfHjy/RcD1+6d4xihc0s+Vsnc/cNsh626fpBbTckPWWx5G5OgLI\n2zqDOeuS9zE0lbFiCr3h4AQhuhvghxIvkLi9nYeqKAznTBQlaXxLSPhhbOYbcgZ459KQhBuGQjbF\ng3fsYPtoGVVVcPyQ83OrHDp6nrml6pZCSgC3jxf4xG39TJRSWLpKxw85u9TipTPrfOn5Wfy3hXnS\npsZn9g8wlrdIGSohsNhweHmuwQtTVZYa7mXfJ21ojBZSFCydlK6iKYJARvhB7N0wV+9ydq2NHyZh\npYSEH8Zmdgy/CzwjhHge2PhGSil/65qtKuHHxgfv3MmR47PMLq2zVu3QaDs898oZ9mzvJ22b5LNX\n5vQG8XyjD+wo8/pcna4fslh3qDsBp5ZaBBKGixafuWP4olDVWDHFx/dU+OYbS8zXJE4QMd/o8vTp\nNRDw8K4KOetScx5TU6ikdVbaLpYEv5eEBoWGDAilRCiC7aV0Mok1IeEd2MyO4b8AjxE3tb10wSPh\nBqRcyHDP/m3sHBtA11WCSDI1t8ZLR8+zsFLb8q4hY+t86vZhDowXGC7ZWJpCyws4t9zie0eXeH2m\nfslrHthR4cEdZQZyRq9SCWbqDj84s86h8zXcd0goF2wDS9PQFAVDUxBIAhkShJJGN2C56dJN+hsS\nEt6RzewYdCnl5U1GE244hBDcuXec107OMjW3ymq1Tafr8dyrUwz1FygXMtwyOYihb+ajczG7BrP8\n4j1jZEyd56JVptc7tLyA4wsNvnxohkrWZLD41o7E0BR+4pYBVAHfP7HKfNPFDSJmqw5PnV6jZOsc\nHMtfkhS3NJWcpeGFIUEoCJGoQiEirpqoOwH1bkA6sQNNSLgsm9kx/IMQ4teFEENCiNKbj2u+soQf\nGwPlHHfuHWf39gEsUyOIIhaWazx75CwLq3VOnV/eUuMbwK2jOX7h3lHunixSyeggBdW2z9HZBn/5\n9BQr9Yt3JClD5ZG9Azy4s8RAxkBXBd0gZKba5bGTK0xXO5f0KWiqwlDOxNY1NFXF1GMDnyiKx2VI\nCbV3aJpLSEjYnDD8Cr08A2+FkV68lotK+PEihOD2PWN88I6dDPblAYEfhhw/u8Arx2dptLrMLK5v\n+fzjlRS/cO8ou4fyFHuWoHM1h0Pn1vl/nz5P823dypqq8tn9w9w+mqeU0lGEoOb4nFrp8OL5Oqtt\n70rQOBMAACAASURBVJL3KNo6Q1kTS1MBgaIqRJGk44UEUUSt4xMlPtEJCZflR8YDeoPzEm4y+kpZ\n9u0cZna5xnq9TaPp0HF9Xnj1HEOVHIauUS5kyKYvLR3dDJP9WX7pA6PU2x6RjKi3A+ZrDi+cWaeS\nMfjcAxMX2YJqqsInb+2n0Qno+k2aTkDV8Tk8W6OcMbhnQiV9QU+ErqqMFmz8KGKxEeJHoGmC0Ilw\ngoimF3B0scFQzvqh3g8JCTcjmwoUCyEeALZd+Hwp5V9eozUlXAcIIbjj1jHmVmocO73A0dPzOK7P\nzOI6R07MUCllsS2DW7YPbLkv4K7tJT59sMu3Xl3gTNSi5QYs1Lo8dWIVoSj84r2jpMy3PqKjhRQf\nv6WPtY7PmdUWXiiZrTq8MFWlL2MwWUljXyAmuqowmLUQwGrLJ4oiJCFBJPHCkGrXww1C2l7ASM5G\nT4x9EhKAzXU+/xWwAzgCvFkGIoFEGG5wUrbJg3fs5OzMKgsrNZbWWzTbLkdPzjNUydNXzDC/Umd0\noLil82uqwsN7+/EDSRgucHqxhRNEnF1poWkCLwj53P0TpK23Pqa3Dma5dyLPWstlrePT9kNOr7Y5\nutDE1FRG8ha28ZY4WJpKMWWiKYLpWoChCLxQ4ocRXV8iZYDWUfBDybZS6hKnuYSEm5HN7BjuBvYm\nvpo3J8P9BR6+Zzenzy/R6pyn1fFYrbZ49eQ8k2P9KIpCIZsik9paOKacMbl/V5m2F9B2QxaqXTpu\nyMmFJkhJMW3wM3eNoPUu2Jqq8MBkhXNrXY7MNmi5AXUn4JXZOoNZg0hKRgs2qZ44GJpCFg1dgZoT\n0PEilCBCFRI/DJFSwdQiIGCl5TKYs5L+hoSbns3cHh0FBq/1QhKuX+7eN84H79xJXzGLrqm0ui5n\nZld5+Y3zdLoe5+fXtmTq8yYTfWnu21nmgZ1lBos2hqbSdkNOLbV55tQqz51eJ7wgUVxO6zy8q8xo\nwURTY5/buZrDofM1FusOc3UH74KqKVNTyNsG5VQ8ntvUFBShxv4PSNp+gBNEtLzgkpHgCQk3I5vZ\nMVSAN4QQL3Bx5/NPX7NVJVxXaJrGT9y/l2NnF2m2HVZqbVqtDiemlhis5Lnv4CQzizUmhrdexbx3\nJEfbC5FInjm9zly1Q8sJOD7f5InjS0RS8sCuMpqqIIRgd3+W+7YVWWl5VLs+fhhxcrlDEEk+OFkk\npSuMFC7u0s6aBrbuAgFOEKIoCirgB5IuIU0XvCDC1NTLrjEh4WZhM8Lw+9d6EQnXP6ODRT58z26m\nZ9doOx7trs/8cp3j5xbZNlpBCIFpaFsatAdxsvveyRK6InADSdsJWG97rLc8XjhTjX/nBty3o0wx\nY2AbKreN5Dm/3uWl2XovFOVzajm+489ZBnlbJ3NB8jplxP4NoBHJ2LdBIPEiiQwkmhf3N1i6muQa\nEm5qNlOu+sR7sZCE65+P3ruHV0/MMrNcxfcDqo0Op6dXOHpqjnzGZn65RiZlbjnfAHBwooAfhKw2\nHV6dqdHshqy2XF45XyUKI1Qh+MSBOLI5lLO4azxPzQk4s9qm5QV0vJBTy20KdpWMpWHrKqoS5wwM\nVdlITIeRStMN0FWBisQNIgxVoePHOYuirW+8LiHhZuNH3hYJIZpCiEbv4QghQiFE471YXML1hW0Z\nfPqhfewc7ydlmXhewFq1yfGzC8wsVpFSMjW3SnAVpjhCCO6eLPHIgSF2D+XI2XED3ErD4/B0je8d\nXWJqJXaWNTSFPQNZHpgscctghoyuEkhJywt4bb7BGwsNVlruRefuSxuoqkLa1MiYKhcu1Q0jmk78\ni46fTGBNuHnZzI5hwxlFxENpPgvcdy0XlXD9snfHCA/dtYu1WouzMz6NjsvCSp1XT8xQzNv0l3Ic\nP7fIrZNDqFsMxyiKwkf39rPe9lAFHJ9v0XB81poer83WeOz1JT6yr5/tfRkKKYODo3lsTUVIeGWu\niROErHc8njlXxdZUUoa6MYm1mDIAWGi4ZEydIJJEXkQYRUSRoOH4eEGIAEj63hJuUq7omytjvgZ8\n8hqtJ+E6R9dVPvqB3ezZNkAuYxFGEWv1DsfPLXL49RlaHRfPDzk/v3ZVXsuqqvAzd43w0C39HBwv\nYOsKYSSptnz+8Y1lnj6+wvnezqFg69w6lOGeiSJjeQNFCPxIMrPe5amzaxyZbVDrjdlQhKCcNhnI\nGGiKwNZVFKEQRSBlHFJaaDi03OCiSqiEhJuJzTS4/dwFhwpxX4NzzVaUcN0zOdbPBw5sZ36lTsf1\nabcd1mptjp9dpFRMc/fecWrNLrNLNcYGt9b8BrFv9E8dHKaU1ql2XI7PN3GCiNlql+++voKmKZi6\nymDBImfp3DqYYb1Tou4tsdIM6AYh59a6/ODMGpGU3LetiNXrjM5aOk4oiSJJVwtRVYEbRkgJLddH\nEYLBrImqJBVKCTcfm6lK+qkLfg6AKeJwUsJNzIfu2c2xs4vUmh1m5qvUmx1W1hucOrdIXyHDtpES\nK+tNKoU0tmVs+X0sQ+WjewdYrHWpd+KRGS03YHqtxaOvgaUrPHJgGNtQ6cuY3DtRpNYJeGFqjbVO\nQNcPOLncRhWCvozJ7v40uhr7NGRNjTCKcMOQlqfgBCEhsN4N8CSstD0ypnrRWG8B2EnVUsINzmZy\nDL/2Xiwk4f3FYCXP3bdto9bo4Lg+C8t1ltab5JZsTk4tYegqw/15phfW2b1t4BLPhCtBCMHH9w8y\nV3U5PLXO3HqXthMytdzisTeW2V7JcHBbESEEw3mLh3aW8YOQF2fr1Do+TTfgjaUWA7l1TE2wsy+D\nIgQFWydtqHS9kI4Zu7v5QQRC0HICmm6AF0YYmoJ2QYVS1w/JWbER0FZQhEgqnhKua67cbSUhocfD\n9+xmemGd9UaHesuh03GZW66STsUWoGEkGRsssLjaYKgvf1XvNZCz+dUHxrENhSePrzK3Hu8cXp+t\n873XF6nkTEZLKYQQbC+neHBnmW4QcnimTtuLaLkBz52rYaoKlqYy2jME0lWFYkqn68cTWBuRRxBK\n/EjS9QN01cDxL+2G7gYRGWNrXx9LUy7qr0hIuN5IPp0JWyadMvnMh2+j1uiyWmszs7BGrd5lemGN\ntGVg6Gpc3YOgkLWvKqQEMFxK8bn7JgD41pEF1lpxeekzp9cYLqT4if0D9OUsVEWwZyCLG0gcP+TV\n+RaOH7He9fjBuSqqIrhnW5HRgo2mKpTSBusdnyLQdn0iBSIp6fr+RXf2UsaPtKkShApeEMIWbvyF\nkGjBWy80e93cCQnXC9dcGIQQjwB/TJy4/ryU8t+/w/PuITYD+iUp5Vev9boS3h3Ghsp89qMHmF5c\nw3E85lfqLK810VQVw9QwDZ3VWutdCSkBlHMm/+QD46w0HZ48tkrbC1msujx6dBFdE3zolj4G8jaq\nIrh9NEckI9p+yKnlDl4gWWq6fP/kKg034NbBHHeM5rF0hUraoOkGLDUVQhnFVqBSQUpxUXWV48fe\n0aW0jhdurWpJyggp3+qTMFLKVvQlIeGasZmqpDLxWIwPEo/bfhr4t1LKtU28VgH+BPgYMA8cEkJ8\nXUp5/DLP+yPg0Sv9ByT8+Nm7c5ifuH8vrhfQ7nrUW11Wqy1OTy2TS8V38IVsirnl2pZHdF/IQN7i\nMweHmVrucmaljReEnFtu8+iri0gJ9+wosa2SRhGC/cN52l6A6y8zU+3iBpLVts8z56rU2j5OEHLX\nWAFLV1EUhbSuxqEjAR032Jjq+iYh4PgBqUBhIHt1O6CEhOuVzewYvgA8Cfx87/ifAl8EPr6J194L\nnJJSngcQQnyBuKLp+Nue9y+BLwP3bOKcCdchn/7QPvwgxHF9TpxdpO34zC/XsC0d2zIwdA1VEUSR\nZHzo6i3D79pe5iP76rivhMyud+n6IScXW0RyIfZ1Bib7Mhiawv3byygoPHp8mXNrHZwgot4NeHmh\nQQB0vZB7JgooikLW0mi4AQBCEURRhK4qSBnvFoQCqoCWGzBWEO9KCCjZLSRcb2xGGIaklP/uguP/\nSwjxS5s8/wgwc8HxLLFYbCCEGAZ+Rkr5ESHERX9LeP9gmQY/+eHbWFlv0O66nOsN2zs/t4auaZQK\nadbqbRRFQUrJ6EBxy53RAKoi+Pm7x+i6EY8dW2Gh2sXxQ04ttnCCebpeRBBE7BrMoqsK90+WyFgq\n33htiXNrbdpeSNeNeGOxiR9GOH7EgZEcGUNDVZSNfELLDdE1iYIglBGdToSmitizQcJWdEFXRTLB\nNeG6ZjPC8B0hxC8DX+od/wLvbsjnj4F/fcFxcgP1PiWTsvjsx+6g0XLoduMdQ6PtMr2wyslzOWxD\nJ9VLQLc6LjvH+zANfcvvl0sZ/Nzdo0RS8vjxVRarDs6bYaVXFoiiiEjCzoEMmqpwYCSPqSt8//gq\nR+Zik5+mE3BquY2UsNp2uWM0S8ZU8SKJpSpc+HFUUWkSEQYRighpeSHGluxAFZKipITrmXf8eAoh\nmsQ5BQH8K+C/9/6kAC3gdzZx/jlg/ILj0d7vLuRu4Au9OUwV4FNCCF9K+XdvP9nv//7vb/z88MMP\n8/DDD29iCQnvJWODRT77sYO0Oi5PveSzWmuxWutw/Owi/eUciqIwPhTnGU6cW2LHeB9pe+tDiQaL\nNr9w7xi6qvD48RVm1jt4fsTUaoe/OzyPF8bisGMgjamp3NKfJW/phFHEsaU2jW7c53B6tUUQpWm7\nIRMVi/60SdHSCHrJ50hKvFCiCvAjcIOQxUYHXb38nb+hKRtd1m9HoJFJ5jAlXCMef/xxHn/88as6\nh7iWjp1CCBU4QZx8XgBeAH5FSnnsHZ7/58A3LleVJIRI3EXfJwRByEtvTPNnX3maQ0fP03V8LFPj\n4C2jfOju3aRTJuNDJQxdQwhBfznLUCUWja3S6Po89voyX3p+mtlqF6dn+lPJWHxifz8f2zfIZH+G\nTM8/eqXp8oWX5ji+1KTpBrTcCFtX2F5JkTFU8rbOvRMFxnu9EQB+EHF0sU7HCwFB2lLfcdiYqgiG\nczZp41JxSBkqBTtJXCe8NwghkFJeUSRmU99EIcRPCyH+797jM5s9uYxr8n4T+A7wOvAFKeUxIcRv\nCCF+/XIv2ey5E65fNE1l385hPn7/rWwbKaMqAtcPODm1wvOvnkNTFeaWajTbDlJKllYbnJxaJrwK\nW82crfPIgUF+7cEJdg1ksAwVgcJK0+Efjizx5RdmOLHQoNb2AOjLmnzunmH29GfI2wZ5S8MNIk4u\nNZmtdal2fJ49V+XUcouMoVBO6VQysfmPEAIh4qF7sdXPpY8gAicIMTT1ksdWO6YTEt4rfuSOQQjx\nR8TVQv9f71e/Arwopfzda7y2t68j2TG8z5hbqvKFb73IN598leXVJkJAPpvi7tsm+PgDe/H9AF1T\nGeztFnJpix3jfVdV6ROEEcfm6nz+8XO8PF3D8SJCKSnYKg/s6uen7hzitrHChmFPo+vzxcOzHJlr\nEIQRLSfECSJsQ2WybNOftdjVl+ZDuyrYusq5tRYztQ5eEM9p0tWL1+pHvbEaxFNft5fSl6xR1wS2\n/lYUV0ma2xKuIVvZMWxGGF4FDkopo96xCrwspTyw5ZVugUQY3p+cOLvAf/mbp3n52HmW11qommCg\nlGNytMKDd+6kv5yj1XEZGyyhaQrlQuaqvKPfZGqlxX/67mkOT9VpuT6hlGQtjQd2lvnl+8fZN5rf\nECAvCPnaq/M8e7ZOxw9wejaiCjBRSTGQMdnVn+aD20tERKx3A95pc9PyfNbb8YhvS1cYK6QueY6p\nKdgX5B9KKT0Rh4RrxjULJQGFC36+uqE3CTcVu7YN8FMP7+fA7lFK+RRBELFWa7OwUufbP3iDV47P\nYhoas0vV3t9anJlewfX8q3rfbX0ZfuuTu3lwT4msraMKQaPr84OTq3zjyCzLjbec3QxN5RfvGOU3\nHhpnrGhhaYKsqSEEnFvtcHK5ybGFJk+fWWe+7hBG8cX9cg/jghLcKPFzSHifspmiuT8EXhZC/CNx\nhdKHgH9zTVeVcMOgKAr37N9OEIQ0Og7a7BpLaw2mF6oUcx6e79N2XG6dHGJmcZ1tI2XqrS7Nsw7b\nRsoUspfecW+W0XKKf/7hHUTAsyfXqHd9Gk7A94+uMlpI8YkDQwzk42F6QghuGcjx2x+2+PprCzw7\nVUdV4ka21baPEzRBQCgj+rImwwUb8zKlqpK3PKYRYKiXNsFpithS/0NCwnvFDw0l9UpIR4l9GN7s\nSn5BSrn4Hqzt7WtJQknvY1zP51tPvsbfPfYqa7UWs4s1XD8gZRuU8ynuuHWMybF+xodL9BU33GSZ\nGC5RLmSu6r0Xa13+7IlzfOfoIm03RErJYNbiQ3v7+Ok7h9k9mEPpXczdIKLe9XlpuspTp9dYbnm0\nvAA/kGRMle2VNJNlm1sGs+wfzl8UEopfH3B4tkEkQVXglv7s5ZZ0EXlLS4boJVwzthJK+qE7Biml\nFEJ8S0q5H7ikryAhYbOYhs4jD95GNmXxradeR9U0ZhfWaXc9/CDgB4fPEknJ6GCR1WqTUj6Noiic\nn18nCCMGyrktv/dgwebXP7qDasvj2dNruGHEYtPh7w/PM7/e5X/56CS3jcbRUlURKIrgnm0ldvWn\n+dsj85xY6tAmpONFTK118YIIgSAIoT9rMJAzyZpxo54qFBQFohAiGecvflQZ7pvNQgkJ1wubyTEc\n7k0+TUi4KmzL4OF79/BrP3M/H75rB7u39ZPPWHh+yGq1xTMvn+PoyTnCMGJuuUbXiUtL55ZqzC3X\nruq9K1mTf/nJnewZzpIyVBQhaHshz5+t8iePnmJ2rQPEYZ60oaIrgr6MxefuHWNXf5qMqWGogrYb\nMF3t8sZii2NLDY4vtTg0VePUSosgjFAUgaEIBPEF3w0i/DC86BFGWy/LTUh4L9hMVdJxYBexpWeb\n+OZGJlVJCVfD1Nwazx45w9OHT/PysWmq9Q5CCHZP9HPfwR2MDRUxDZ3+UpZMKm4TLmRtxodKaFcx\nZ+j8Sou/enqKZ06tstIKiKIITVW4e3uB//Oz+xjsGfhcyHS1yxdfnGVqvU2tE+CHEttUqaR1srZB\n2dYYKVhkLZ1tZZu6Ez9HSsiayiU7BkO9uCppKGugvkMHdULC1XKtylUnLvf7NyemvlckwnDjUW92\nOXxsmv/xjed4+fgMna6Prqv0FTPs3znM/lvGyNgmuaxFfykOJaUsg10T/Vc1gM/1Q/7u8Cx/8eR5\nlhouUkpMTWXfSIbf+dQe9owWLnnNQr3LV19Z4PX5BnU3JIgkpipI6SqmpjKYMxgtpsiaGoW0tjEq\nw9KVS0pRjbcN0dtWspOmt4RrxrsqDEIIC/gXwE7gNWKTneCqV7lFEmG4MfH9kCdfPMEf/9VjLK02\n6Lo+CgLD1Ng7OcSd+8bJpCwGyhkGynlUVSFlGYwPlUhdxVgJKSV/f3iOP33sNMsNDyljm8/RksX/\n+vAkH98/dIkvc73r8+fPTnFmrUPLjfCCkFCCQJAxFQZzFqMFm2JKoy9rYusqtq7ydntnVREYiTAk\nvEe828LwRcAHngI+BZyXUv72Va9yiyTCcOPi+yFf//7LfOX7R5iZW6XZ9QmCEMsy2D3Rz227hhnq\nyyGEwvaRMrmMjRCCscEileLWK5aCMOKrh2b4y6fOs9RwiHriUMzo/JMPjPCrH5xEf9vOpNp2+eLh\neaarXbp+RN3x8fyIUMYJ6YGswf6RHJWMgaVrTJQtbO3iGg9TVUhdMEMpbahXNScqIeGH8W4Lw2u9\naiSEEBpxmeqdV7/MrZEIw41Np+vxxKGTPPvKGZ575Syr621cP8AwVHaO9nFw7ziFbBz/3zHeR18x\ngxAKQ315hvq23nPphxHff22Rv3xqitMrHcIoTiCnDJWP3drHv/rUHnKpi3cmUkpeX2zw0vk659Y7\nLNRdnCDECyL8UDJesjg4kidr6aQMlVsHM6SMt8TB0hQyydzthPeId1sYDl8oBG8/fq9JhOHGx3F9\npuZW+dYTr/G9F06ytt6k03URQrB9vMLeySFGB0rYlk42bTI6ECeoRwYK9JeyW+4F8MOIH5xc4SuH\nZnnpXBXXj1BEvHvYM5Thnz88yf07K5fYfALMVzt88/UlXl9ssdbxCEKJRDLcCysN5S36swYHRt6a\nHmtpCmkjEYaE94Z3WxhC4iokiCuRbKDDW1VJWy8s3wKJMNw8LKzU+eK3X+TxF44zt1Qn8EOCKCSX\nSXHL5CC37RzGNFSKuTQjA0UyKRPL1JkYLm3Z2yEII04sNPnbQzN89+gyLTdACFCFIJ/S+bl7R/mf\nP3xpaAliYXl5tsZfH5phpRMgI0CCpgomSjYHRwscHM0zWdl6F3dCwla5JlVJ1wuJMNxceJ7P175/\nhL/5zmHml2p0XR/H9TEMlT3bBtm/ewTbMukvZRkdyJPPplAUwc7x/o3y1itFSslCrctjbyzzV09P\nUW0HhFGEqsRVRI/cPsAv3zfOZH/msruT586t88WX5qg6PkEo8YJ457FvMMPtowX2DmXZXk5fktRO\nSLiWJMKQcEMhpeTxF07yP775PGdmVqk3O3heQBRJhvryPHj3LgYrOQxdI5+xKeRSDFZy7Nk2cFW9\nDvWOz5Gpdb70wixHzldx/AghYle2kYLNT909xM/eOUbWvtiWVErJ4Zka3zm+wsxal7rj4wSSlKHw\ngW0FBnI2YwWTXX0Zimkjmaia8J6QCEPCDUm92eEL3zrENx5/jdVqi07XxQsC0rbJXfu28YED22l1\nHMqFNMVchpGBPOVChuG+/Jb7HTpuwMx6l0dfmeNvXpin4wcIYu+ElKly+1iBf/bQBHdMlDbmLL3J\netvlu8eWeezkGk03wAslwzmTHX1pCimdsUKKkaJF0dbpyxjJnKSEa0oiDAk3LFJKvvfcG3z+y89w\nbnYNx/VwPR9VVbh1coj9e0axrbhTOp+xGSjnyKRMxodLW57QKqVkueHwtRfn+OaRBZabLn4Q9XIP\nCv0Fk588MMDP3TNOf9666LVdL+A/PzXFa4sNPD8CBOW0TiVtUskaDOVNhnMWIwWb4byViEPCNSMR\nhoQbnpX1Bv/+84/y3JGztDoeYRgSRRG6rrN9tMLd+yZIp0xSts5QX4Hhvhz95TxDldyWw0urTZfT\nSw0efXWRp46vUnd8oujNRjWF20dz/B+f3cdI+WIBOrnc5L8+PUW1NyJDSLnh91xM6eRsjdG8ze7+\nDGNFe8NVLiHh3SQRhoSbgq7j8bXHXubPvvIMq7U2YRjh+wFSSnaM9/Hph/bTdjwiKakUMuzZPkA+\nY7Nroh/b2lq3dBBGLNUdDp1Z5yuHZjmz0tooa9VUhd2DOX7z4zu4e0dp4+5fSsm3X1/iidNr1Ny4\nEc4NwNYFeVvD0jUKtsa2coq+jMlEKcVI3sK4jM9DQsJWSYQh4aZBSskzL5/hy99+kcPHZ6k3u/hB\nSBCGDFTyPHTnTtK2ASI2xdmzbYC9O4bZs33gqsI2jhcys9bm64fn+M5ri6y3faSMdw95W+ehPRV+\n8xM7KWXi0JLjhzx5eo3DMzVW2j6Nro8ThCAFqoCMqdKXNSmmdIbyFuNFm4liikrGuGzfRELClZII\nQ8JNR6fr8fqZOf7dn36LmYUqUkrCKCIMI/pKae47sIN8NoVQ4LadI+ze1s+2kTKmof/ok78DUkrm\naw6Hz63xn79/juVmlygCRYCqKOwezPCHv3SAkVIcWup4ISeXmhyarnN2rU3LCXDDiLYToKgCQxVk\nTJ2cqVHJmkyWbLaVU4yXUuTtra8zIQESYUi4iTl1fpHf/Q9f4+zcaq/BTMYmOQL27x5l97YBLFPn\ntl3DjA4WmRytkMtcOmL7Smg6Pi+eWedvXpjhlZk6jhcC8e5hMG/xW5/cycf2DW7sUFquzxMn1zi2\n3GSt7VNv+3SCkCCM5ywZmkLO1hjKWQxkTbK2znjBZv9I9qJprAkJV0IiDAk3NVEU8Rd/+wx/8+hL\nrFRbhKEkkhFdN2B0oMCDd+7ANk36Smlu2T7I2FCJkf7CVfU8uEHIUt3liWPLfPHZaRYaDvRCS5au\n8Mn9Q/yLj05SzsWhpTCKmKs5nFhqcWSuxlLDoxtEtBwfP5QoiiCtq5QyBuNFm7ShMpy3+NCuCkYS\nWkrYAokwJCQArbbDn331B3zp0Zdodz2CIKTjeIwNFLj/4CSZtI1t6uzeNsD4UImRgcJV+0o3uj5v\nzNX5j98+xZnlFmEUf1Y1VWEob/HrH93OIweGNuYlBVHEmZUOR2ZrzNQcVlsu1bZPNwhBxl9mXRX0\nZwx29GXoz5pMllPs6s8kyemEKyIRhoSEC3jixVP8hz9/lJnFGkEY4nkh/eUc+3YOMtRfwNA0xoeK\n7JwYoJC1Ge4vbHmcBsS7h3PLbf76mfN8740lHC+28FQUgakp3LujxP/+6T0MFd8qa/XDiLOrbY7O\nN3hjoclSy6PthQgJbhgSSaikDG4dypC1dCoZg0IqDjEl/Q8JmyERhoSEt7G4Uue3//BLnJ5eJook\nfhhgaCqTY33s2TZAyjbJpS12bx+gr5ilv5xlsLz1ngeIR2o8+cYif/H0NNPrHaJIxolpoTBYsPin\nD4zzqYNDZKy3EsttL+Cl6TqnlltMVzssNT0cP8TxIxCSjKkzXrTYVkohRCw040WbtKlhaQp5OxaN\nZMxGwttJhCEh4TKcmlrk3/7pNzk9vYLjBoRRnPAt59Ls3jbAtpEyqqYyUMly6/ZBUrZJKZ+mkLXJ\nprd2V+4FEedXWnzp+Rm+e3SRphNuTGtNWxp3jOf5Zw9t58B4YeP8jh8yX3OYqzvMVjscnm2w2vZA\nxqEnRYHhrEk5a5E1NVKmigooQsHUFYZzJgdGctjJSO+EC0iEISHhHZhdrPL/fPlpnnzpJLVmFxnK\nnuuawkBflh2jfQxWcmTTFrdODlHI2Ri6hqFrlApphiq5LbmsLdUdXjq7zuefOMvsukPQm9Zqvsnz\nMgAAIABJREFUaCpDBZNP7h/kru0ldg1mNnYQTSdgreNxarnJE6fWWGn5BGFEJCUREgWBrcf9D5am\noAlB2lLJWjoDGZOhgsW2kk3WSkpdExJhSEj4ofh+wHeeOcYX/+EQp6aXcZwA3rzYCkGlmOGOW0cp\nF7PYpoFt6uyc6COfSWHoKsV8mv5iFl2/sjBTte1xerHJ11+c5enT67TdENF7z7SpUslb3DtR5JGD\nQ+wezKD3wliLDYdza21emW3w2nyTjh8igFBKojBCVRUUAZGMdyIjRZuJokXONkgbKgVbp5jSSZsq\nWVOPRUQVaIpIchM3EYkwJCT8CKSUHHptimdePsNjz59gbrlGJCVSRnheSCFvc3DPGCMDRQAMTWXX\ntn5GB4ooioKmKlsardH1Qs4stzh8bo1/OLLEudV2vHsQAkVRMFTBWNnigV0Vfvn+CUoZk0hKlhou\n9a7H6wtNXpyu03RCvDDEjyRSQvyNkHh+hCIEfVmD4bxF1opzD4amYGoqWUsjY6oYqoLeq5TqTya7\n3hQkwpCQsAmklMwsVnn15CyPP3+Co6fnWV5vEgQhUSgxTQ3T0MilLfbvHqVcyGDoKgOVHAPlHMWc\nTSGbplLMkM9uvknODULOr7SZWe3wjVfmOTHfpOEE+EFcvSQAy1C5YyzH5z44wa6h3EY4qNr14/xD\nrcvZ1TYLDZe2FyKlpBtEeIGMzyPA1BQsTcE2VUxFoCpxcnoga6KpAkVAylA5OJJnrJS4yt3oJMKQ\nkHAFtDouZ6aXmZpb5dtPHeXIiTkcx0eo8XylrhNgmRp7JuIdg2nqVIpp+kt5MimTfNbm1h1D9Jey\nm35PKSWrTY+Z9Q7H5uu8MdPg8HSNesfHDUIU4rEaOVtnopzi4Hiee3eVmajEXg6RhK4fsthwqXY8\nukHI+bUur801aLghQRSLjIwkmiKQAiIpUYXA1lRUVUFXoJAy2NWX4c6xPOW0Tt7Wk9lMNyiJMCQk\nXCFRFLFSbTEzv873nj3O0y+fZr3ewvECZChxgwAiSKdNKoUM40NFJobLpFMmhqExPlTiQ3ftIpu2\nfvSbXUDHDVhpuqy1PJbrDk+dWOGFM2vUurFDnaoIBKBrCkVb484dZT552wAHxotkrIurjpwg5ImT\na7w0U2Ol5dLxQkIZixACgvgATVWIZIQfSlRFoZIyGMpb9GV0KlmTStpgz0CGgr21CbQJ1yeJMCQk\nbJEgCDk/v86Z6WXOza1wenqVQ0enaLQcoijaCPdYpsZQpUB/OcNwf4Fsxmb3RD97dwxTzKfIpS0s\nc/PVQGEkqXU8Vhoux+YbfPXQDGdXOvhBhCS+uAsRJ4xLaY1fuGeMh27pZ7BgXyQQUkoW6w4vz9aZ\nrnWpdXw6XogXSrp+SNuNw04QC4kXSExdIWWo5E2NnK2RNjVG8haT5TQ5W6OQ0kkbKoqIk9zWFSbd\nE64PEmFISLhK6s0uCyt1Oo7H66fmeOyFE8wuVKm3HbqORxDG5aZCCFRV4Z7bJtg+2odpaBu9D4N9\nOXZPxB4QV9IoV+/4zK53eP7MGi+eqzK73mG95eEFEWGvSc7SNSb7UvzkncPcPlagkjMppAxU5a1e\niGrHY67u0uz6dP2QetdnvRPgBCGuH7HYdKl2fKIodpYTgKYLLFUha2kUbANNFeiKQtrUyJgKuirY\nO5Bl10AmGej3PiMRhoSEd4kwjPCDkNVqi2cOn+GlY9McOjpFrdEhjCKiMKLj+Vi6xifu30tfJbfx\nWiFgpL/A6GCR8cEi20f7Nu09HUWS9bZHo+szX+3w6nSNp0+ucW6lQxBFSClRhELOVtk5kOWO8QLD\nZZvhgs1g3iJr62QtjUhCyw0IIslKy2Wt7RFGEi+ImKp2OLfapdETjjCKQ2peBKoAQxOoQkFV44uK\nqiioAvKWxmDOpJQ26cvocZWWEOzqSzOYv7JQWsJ7x3UpDEKIR4A/BhTg81LKf/+2v38O+Ne9wybw\nv0kpX7vMeRJhSPixEEURy+tNjhyb4bHnj3N6ZpmZ+SquH+B6IZmUweRoHwPlLCnLJJ+zET2DoHzG\n4o5bJ9gx3kchGzfNbRY/jOi4IbPrHf76mSme7fVAhFIiI4mqKth63PVsGypFW2e8L83+sQK3DGUY\nKaXJ2zqKInD8kCCSRFIyV+tyfLHFQsNhqenRdgO8UMZCEkokkiiKS2E1hY1KJl1T0RWFlK5i6gpC\nxJVUuqowVrQZL9rkbJ2cqcYDALNWEn66DrjuhEEIoQAngY8B88Ah4JellMcveM59wDEpZb0nIr8v\npbzvMudKhCHhx06t0ebM7Bpf/e5LfPvpN4h6iV0EKEKgawqFXIrd2wYp5dIYpoaqKpTzaQYrOUYH\nihRyNn2lLClr830ES3WH50+v8b2jSxxbaNDsBr3+C6B3gQaBpkDK1MhYGuOlFA/tqXDHtiKVrEmu\nJxJhJFluulS7PstNl8W6Q9MNWWh0qXd9kIKo1xsRSkkYSTqeRAqJoQh0TWCoGooS3+0ZWtzJrQgw\nVIGpqaiKoJzS+fitffRlzCT89GPkehSG+4Dfk1J+qnf8bwD59l3DBc8vAK9JKccu87dEGBKuG5rt\nLn/wn/6eJw+d2khMAyAlfhBiWxqWaVApZJgYLWPpOralk8vYDPXnGSzlKBXSDPbl6StmNiUQYSRZ\nqHZ48sQqz5xa4+xyC8cLccOIIIjv9EMpEQgUIVCFwDIExbTJQN5k73COTx4YZHt/Bl1V4tLZtsda\ny8MPIxZbLkt1N05We/Fk1yCMiCLJasfDD+Lzu0EYJ8YBz493IZauoIi4FNbUFBQFbF3DNlQmyynG\nizYf2FZMSmJ/DFyPwvDzwCellL/eO/5V4F4p5W+9w/N/B9j95vPf9rdEGBKuK2rNDt9/9jiPPXec\nc7MrNLouna4XO8gBfhAQRRH5bIpiziZlW2RTJrmMRV8xQz6XplJIM1jJc8vkIP2l7KZyEVEkma91\nOTpTZ77aZaXpstToslR3WK67dPy4iiqM5EZISxECW1foz5mMlVOMl1PsG81z+1ieXNqg40U0nYCO\nF+clglCy2Oyy2PBouSF+FOEFIU0nxA/flAWQUUStGxBEEBEP+0NKgij2oujP6NiGhqkKbhnMcvtI\nnv6syZVYSqRNLZkaexW8r4VBCPER4E+AB6WU1cv8Xf7e7/3exvHDDz/Mww8/fK2WnpCwaerNLivV\nJqfOL/PdHxzjtVOzNFoOnh/2LpIRmqKgqArlXIp02sQydfqKWcqFFLquU8ylGKrkGB8uMzJQIJuy\nsC39h+4kokji+CFdP6TjBdTbAcfm6pxeanF2tc35lRZNN/a/jq/lsUOcrsYjONKGStbWyad0Rksp\n7ttZYnt/FlNXsAw1FoQwTnjP1hzW2h4tJ6Du+LhhHMbyw2gj7xFFEV7v920vpO0GKAJMXcVQBf1Z\nk0JKR1fi/MRmKaUMPr2vn0pm614ZNxOPP/44jz/++MbxH/zBH1x3wnAfcc7gkd7xZUNJQogDwFeA\nR6SUZ97hXMmOIeG6x3E8nn75NK+emuPZI2dZXGkQ9HydfT8kDEIUTSGbMknbFilbp1zIkMtYmLqO\naWqMDhQp5VLksjbFnM3O8YFNj95w/ZBGN6DW9nhjvs6JhSanl1ssrndZ6/h4QRwaAjb6IzRFoOmC\njKaSTRtkDZXtAxkGcha7hjKMlFJ4gQQlTqfElUzxOdpeyHS1S9sNCMKIhhMnuVtuQNPxcAPwgvh3\nhqaQNuLcxJVgqAoTJZufvX24dw4VS1d6hbbvjK4mwwLh+twxqMAJ4uTzAvAC8CtSymMXPGcc+D7w\nP0kpn/sh50qEIeF9xdxSlW/84xFePj7H8lqTVtfFcX06XZcwjBBCwdBUDEMln7VJWya2bZBNW6Rt\nA9PQKWRtivkUQ5U8lWKGwUqe/tLmJrxGUdzcVut4nJhv8sTxFWZW26y2fVqOT8cN49LbC75WiiIQ\nCFQFNEWQs3WGCnGJ6kRfipShk08Z9OdNMpaKqasIAV4Yvz4eDw6ztS6Hp2s4QUTbC6l3PQIp4zDb\nFVyienl9bEPlI7sqDBfsjeGAP4qcpbG7P7PR43Gzct0JA2yUq/5H3ipX/SMhxG8Q7xz+qxDivwE/\nB5wn/gz4Usp7L3OeRBgS3pdML6zx+qkFnnrpNLNL69SaHRoth07Xww8ioihEURQURWDo2oYoZFIW\n6d6OolLKYJsGlqExUM4xNlhiYqRELm1vegx4te0yvdphrtbl3FKL6bW4Cqna8lhpxqM0IikJI3gz\nh7CBiCueNEVB1wQZU6c/ZzBRTrOtkmKskmaiL8VQ3sIyNFZbHieWmqy2fU6vtFlsOht5j0huXhvq\njo8TSDQBY0Wb4YKFpamkTbWXdxBYukLBvny3+VjBIm1euXFROa2j9ybRvt+5LoXh3SIRhoT3O1EU\ncWZ6he88c4xT00vMzK9Tb3VxemGYMAzx/ABFUVAVgaopgMAy9Di0lLEpFdMUMja5jE25kGZkoMjo\nQAHbMjANjWzKIrWJWUdRFLFYd1msd5lb73J+tc351Q71rs9y3aHrSbp+gOPHW4HozdlLFyIEhiqw\nDJW0oTJUsNg/nucDkxWGChYNJ4h7LqTk5GqblaaPF0aXaM4PY7rWZr7mAvFIDk1VMBRB1lIvKoEV\nAvQ3RctQiYiT1nlL44q2KD129qXImDo7+9JX/NrrjUQYEhLeB0gpqTe7HDk5zXNHznH6/DKrtRad\nrk/X9TdyEkEYEkUSgYKqSpBxGWoxn2KwL0+5kCafSVHI2ZRzaUzLwDRUMimL4f4CQ5Uc+ay96Th7\n2/FpuSHLjXjW0tnlNq9M11isOdQ7Pk6vS9oP37zzlxtJbXrhJ0tXydka/TkDXVOxNJUDY3luG8+T\ntQ1Utddxsck1nVps8tVXFnDezIv0PCgMVVzWUU8RYGoqYRRh6grjBeuK7/oVRbB3MMtowWZXf+aK\nXns9kghDQsL7jCiKODuzytxylddOzXNubpXVtSa1Vjybqdl2cP2AMI7vEIVxKamqgGXp5DM2feUs\nKUsnkzLJpW3SPc/qbMbCNDSKuRSDlTwj/QWyafOKLUqllKw0HWbX29TaAcfmGpxb6TCz3qHW8ekG\nIZ4XW6VGkUQoAu2CMlmzF+qxDJWRok05Y2IbKrsH00z0Zdjel77sxVsCZ1fafPnleeabTq9vInpr\ncizgR/LSqBexVgUyoi9tXHEoKWuqHBjJc9twjslKsmO4rkmEIeFGJ4oiFlbqzCysM71YZWGlzuzi\nOrNLNRoth1bboeN4BEGEF4axg1sUYRgamqqiqgLb0MhmbHKZFJm00Qs7WRSyacrFNIVMisFKjpRt\nkrJj+1JFEZiG1vt58zOdZtc7HF9ocGqhxfGFBudXY+FwgnDjeW9WL6mKgqoK9N5oDbVXDaVrCsWM\nTtrQGC/b3DaS456dZfpzcRVWEEYcma0ztd4hCGC947LU8nD9AIgFoqeZdHs5DAk4vem0Vq8S6koY\nyZvsHcqxpz/DjiSUdH2TCEPCzUYQhJybW2V6YZ3F1TrVeoeTU8ucnVul0XJwXI/AjwijCImM75Rh\no/OZ3sXXMDVKuTTD/QUylkkua5OydEr5NCnbwDBUDF0jZRqUCxn6StkNBztjkxfVjhPwxnydH5xc\n4cxSh6bj44cRa22PWtvfuGBHkezNWHprR2EZ8TgNAaiqQs7SuH9HiU/ePkgla2EZCmEokULghxHT\n613c8NJrQduLXe6WGg4LDZ8gilAUQXqTyXnRS7BvK6bY3p9mvGCxe2DzJkzXK4kwJCTcoARBSLPj\n0mg5nJ9f5Y2zixw/Pc/Uwjqdrofr+nG/QBD3GIRRhIJAinggniLAtoyNqifb1En3EtWWrqFqCoau\nkk6ZpFMmqV4FVCGXophNUSqkmRguYxk66jvE9wH8IGKh5tByfLpeyOnlFifm6jTckNWmy1rTJeg1\n5rl+bBoUSVCUNwWNeGehCixNpZgysM04b5G3dNK2RimlM1ZOs280T9bW0dQ3x3+o1Ls+TSfgm68v\nsd7xr+j/2NLiAYH9OZOMqTFRtNk9kOQYrmsSYUhIiAnDiKW1Buv1NmemV1irt2i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ZAAAD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20e7ab8f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotResampledByDecade(resps)\n",
    "thinkplot.Config(xlabel='Age (years)',\n",
    "                   ylabel='Prob unmarried',\n",
    "                   xlim=[13, 45],\n",
    "                   ylim=[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can generate predictions by assuming that the hazard function of each generation will be the same as for the previous generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PlotPredictionsByDecade(groups, **options):\n",
    "    \"\"\"Groups respondents by decade and plots survival curves.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    hfs = []\n",
    "    for _, group in groups:\n",
    "        hf, sf = EstimateMarriageSurvival(group)\n",
    "        hfs.append(hf)\n",
    "\n",
    "    thinkplot.PrePlot(len(hfs))\n",
    "    for i, hf in enumerate(hfs):\n",
    "        if i > 0:\n",
    "            hf.Extend(hfs[i-1])\n",
    "        sf = hf.MakeSurvival()\n",
    "        thinkplot.Plot(sf, **options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's what that looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V1FSyPB5Z10Pv8Gs3/LCps6g5yRUdGRJhP2GtULHZ2Jfnmb0D5IpTh7ACJMI6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\njk38/43XaExAvo+IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21044b31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotResampledByDecade(resps, predict_flag=True)\n",
    "thinkplot.Config(xlabel='Age (years)',\n",
    "                 ylabel='Prob unmarried',\n",
    "                 xlim=[13, 45],\n",
    "                 ylim=[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remaining lifetime\n",
    "\n",
    "Distributions with difference shapes yield different behavior for remaining lifetime as a function of age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of complete pregnancies 11189\n",
      "Number of ongoing pregnancies 352\n"
     ]
    }
   ],
   "source": [
    "preg = nsfg.ReadFemPreg()\n",
    "\n",
    "complete = preg.query('outcome in [1, 3, 4]').prglngth\n",
    "print('Number of complete pregnancies', len(complete))\n",
    "ongoing = preg[preg.outcome == 6].prglngth\n",
    "print('Number of ongoing pregnancies', len(ongoing))\n",
    "\n",
    "hf = EstimateHazardFunction(complete, ongoing)\n",
    "sf1 = hf.MakeSurvival()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the expected remaining duration of a pregnancy as a function of the number of weeks elapsed.  After week 36, the process becomes \"memoryless\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cye3PO5lgAwrOkoE5CdPnruS9L2b7JsqJiY7i0dv70a5FA48jMya4SpIMIgLY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\ncYNt7fQQAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20e7ab8f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rem_life1 = sf1.RemainingLifetime()\n",
    "thinkplot.Plot(rem_life1)\n",
    "thinkplot.Config(title='Remaining pregnancy length',\n",
    "                 xlabel='Weeks',\n",
    "                 ylabel='Mean remaining weeks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the median remaining time until first marriage as a function of age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hf, sf2 = EstimateMarriageSurvival(resp6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E/2QZs93dr6lGMK2BF4BvEYyP+zzwnLs/W2o9Jfw08/HClfz1hRl8s2XnAcua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\nRCRNKOGLiKQJJXwRkTTx/2nRWKzWttEsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20e744a6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func = lambda pmf: pmf.Percentile(50)\n",
    "rem_life2 = sf2.RemainingLifetime(filler=np.inf, func=func)\n",
    "    \n",
    "thinkplot.Plot(rem_life2)\n",
    "thinkplot.Config(title='Years until first marriage',\n",
    "                 ylim=[0, 15],\n",
    "                 xlim=[11, 31],\n",
    "                 xlabel='Age (years)',\n",
    "                 ylabel='Median remaining years')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:**    In NSFG Cycles 6 and 7, the variable `cmdivorcx` contains the date of divorce for the respondent’s first marriage, if applicable, encoded in century-months.\n",
    "\n",
    "Compute the duration of marriages that have ended in divorce, and the duration, so far, of marriages that are ongoing. Estimate the hazard and survival curve for the duration of marriage.\n",
    "\n",
    "Use resampling to take into account sampling weights, and plot data from several resamples to visualize sampling error.\n",
    "\n",
    "Consider dividing the respondents into groups by decade of birth, and possibly by age at first marriage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def CleanData(resp):\n",
    "    \"\"\"Cleans respondent data.\n",
    "\n",
    "    resp: DataFrame\n",
    "    \"\"\"\n",
    "    resp.cmdivorcx.replace([9998, 9999], np.nan, inplace=True)\n",
    "\n",
    "    resp['notdivorced'] = resp.cmdivorcx.isnull().astype(int)\n",
    "    resp['duration'] = (resp.cmdivorcx - resp.cmmarrhx) / 12.0\n",
    "    resp['durationsofar'] = (resp.cmintvw - resp.cmmarrhx) / 12.0\n",
    "\n",
    "    month0 = pd.to_datetime('1899-12-15')\n",
    "    dates = [month0 + pd.DateOffset(months=cm) \n",
    "             for cm in resp.cmbirth]\n",
    "    resp['decade'] = (pd.DatetimeIndex(dates).year - 1900) // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "CleanData(resp6)\n",
    "married6 = resp6[resp6.evrmarry==1]\n",
    "\n",
    "CleanData(resp7)\n",
    "married7 = resp7[resp7.evrmarry==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def ResampleDivorceCurve(resps):\n",
    "    \"\"\"Plots divorce curves based on resampled data.\n",
    "\n",
    "    resps: list of respondent DataFrames\n",
    "    \"\"\"\n",
    "    for _ in range(11):\n",
    "        samples = [thinkstats2.ResampleRowsWeighted(resp) \n",
    "                   for resp in resps]\n",
    "        sample = pd.concat(samples, ignore_index=True)\n",
    "        PlotDivorceCurveByDecade(sample, color='#225EA8', alpha=0.1)\n",
    "\n",
    "    thinkplot.Show(xlabel='years',\n",
    "                   axis=[0, 28, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def ResampleDivorceCurveByDecade(resps):\n",
    "    \"\"\"Plots divorce curves for each birth cohort.\n",
    "\n",
    "    resps: list of respondent DataFrames    \n",
    "    \"\"\"\n",
    "    for i in range(41):\n",
    "        samples = [thinkstats2.ResampleRowsWeighted(resp) \n",
    "                   for resp in resps]\n",
    "        sample = pd.concat(samples, ignore_index=True)\n",
    "        groups = sample.groupby('decade')\n",
    "        if i == 0:\n",
    "            survival.AddLabelsByDecade(groups, alpha=0.7)\n",
    "\n",
    "        EstimateSurvivalByDecade(groups, alpha=0.1)\n",
    "\n",
    "    thinkplot.Config(xlabel='Years',\n",
    "                     ylabel='Fraction undivorced',\n",
    "                     axis=[0, 28, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def EstimateSurvivalByDecade(groups, **options):\n",
    "    \"\"\"Groups respondents by decade and plots survival curves.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    thinkplot.PrePlot(len(groups))\n",
    "    for name, group in groups:\n",
    "        _, sf = EstimateSurvival(group)\n",
    "        thinkplot.Plot(sf, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def EstimateSurvival(resp):\n",
    "    \"\"\"Estimates the survival curve.\n",
    "\n",
    "    resp: DataFrame of respondents\n",
    "\n",
    "    returns: pair of HazardFunction, SurvivalFunction\n",
    "    \"\"\"\n",
    "    complete = resp[resp.notdivorced == 0].duration.dropna()\n",
    "    ongoing = resp[resp.notdivorced == 1].durationsofar.dropna()\n",
    "\n",
    "    hf = survival.EstimateHazardFunction(complete, ongoing)\n",
    "    sf = hf.MakeSurvival()\n",
    "\n",
    "    return hf, sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q6HiaXFEDEGP0IvITwKd4pAz0SyLyz9sfx38FvBt4v4j87fLXfibGuHsl13W1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\nzBrKAcDMrKH+D6OWhozTysaWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20e7a96b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "ResampleDivorceCurveByDecade([married6, married7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
